Artificial intelligence-enabled ECG for left ventricular diastolic function and filling pressure

Assessment of left ventricular diastolic function plays a major role in the diagnosis and prognosis of cardiac diseases, including heart failure with preserved ejection fraction. We aimed to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify echocardiographically determined diastolic dysfunction and increased filling pressure. We trained, validated, and tested an AI-enabled ECG in 98,736, 21,963, and 98,763 patients, respectively, who had an ECG and echocardiographic diastolic function assessment within 14 days with no exclusion criteria. It was also tested in 55,248 patients with indeterminate diastolic function by echocardiography. The model was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve, and its prognostic performance was compared to echocardiography. The AUC for detecting increased filling pressure was 0.911. The AUCs to identify diastolic dysfunction grades ≥1, ≥2, and 3 were 0.847, 0.911, and 0.943, respectively. During a median follow-up of 5.9 years, 20,223 (20.5%) died. Patients with increased filling pressure predicted by AI-ECG had higher mortality than those with normal filling pressure, after adjusting for age, sex, and comorbidities in the test group (hazard ratio (HR) 1.7, 95% CI 1.645–1.757) similar to echocardiography and in the indeterminate group (HR 1.34, 95% CI 1.298–1.383). An AI-enabled ECG identifies increased filling pressure and diastolic function grades with a good prognostic value similar to echocardiography. AI-ECG is a simple and promising tool to enhance the detection of diseases associated with diastolic dysfunction and increased diastolic filling pressure.

7. The distribution of left atrial volume index, e', E/e', tricuspid regurgitation velocity, and E/A according to diastolic filling pressure by our AI-ECG in grade 1 by echocardiography.
8. The ROC curves and performance of AI-ECG in normal ejection fraction and low ejection fraction, defined as left ventricular ejection fraction <50% in the test set.
9. An illustrative case of a heart failure patient.11.Algorithm for assessment of diastolic filling pressure and function.
2. Table 1.Patient characteristics of training, validation, and testing group.
10. Flow chart of dataset construction and patient distribution.a.A flow chart demonstrating patient selection.b.A pie chart for dataset split.

Figure 5 .
Figure 5. All-cause mortality using a Kaplan-Meier curve with 95% confidence intervals and p-value from a log-rank test by true positive (TP), false positive (FP), false negative (FN), and true negative (TN) for grade 1 or above, grade 2 or above, and grade 3.

Figure 6 .Figure 7 .Figure 8 .
Figure 6.All-cause mortality using a Kaplan-Meier curve with 95% confidence intervals and p-value from a log-rank test by age.a. Kaplan-Meier curve for test group of patients (age≤50) according to the filling pressure by our AI-ECG (HR 1.413, 95% CI 1.329-1.503).b.Kaplan-Meier curve for test group of patients (50< age <70) according to the filling pressure by our AI-ECG (HR 1.35, 95% CI 1.313-1.388).c.Kaplan-Meier curve for test group of patients (age≥70) according to the filling pressure by our AI-ECG (HR 1.235, 95% CI 1.211-1.26).HRs were calculated with four grades after adjusting by age, sex, and comorbidities.a b c

Figure 9 .
Figure 9.An illustrative case of a heart failure patient.Video for the echocardiograms is attached separately (Supplemental Video 1).

Figure 10 .
Figure 10.Flow chart of dataset construction and patient distribution.a.A flow chart demonstrating patient selection.b.A pie chart for dataset split.

Table 1 . Patient characteristics of training, validation, and testing group.
Values are n (%) or mean ± SD.Obesity was defined as body mass index ≥30.Renal disease includes any stages of chronic kidney disease, hypertensive kidney disease, glomerulonephritis, nephritic syndrome, hereditary nephropathy, end stage renal disease, unspecified kidney failure, dialysis, and kidney transplant status.

Table 2 . Patient characteristics of four diastolic grade groups determined by AI-ECG in test set.
Values are n (%).

Table 4 . Clinical characteristics of normal or grade 1 group by AI-ECG in grade 1 patients by echocardiography.
Values are n (%) or mean ± SD.Student t-test and chi-squared test were used.

Table 5 . The number of patients for hypertrophic cardiomyopathy (HCM), cardiac amyloidosis, moderate to severe mitral valve regurgitation (MR) and aortic stenosis (AS), and reduced left ventricular ejection fraction (LVEF) according to diastolic function grade by AI-ECG.
Percent in each column indicates distribution of a particular condition within the same diastolic function category.